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* Wrap ck host utitlies in CK namespace.
The CK and CK-Tile source code bases are incompatible because CK is not properly using namespaces everywhere. In particular, we need to put hip_check_error in the ck namespace.
Move all functions in include/ck_/host_utility that were in global namespace into the ck namespace.
There may be additional namespace problems like this, and it's possible we'll have namespace clashes. But it is good design to properly guard our to code bases (CK and CKTile) so that they can both coexist. Moreover, estabilishing this compatiblity is essential if we are going to allow the builder to instantiate kernels from either template library.
* Add using declarations to test code.
After moving some of the untils into the ck namespace, most examples and a few tests had to be updated to recognize the new namespace declarations. We add using declarations to individual compute units for functions that were previously in the global namespace.
* Add using declarations to client examples.
[ROCm/composable_kernel commit: ad57f6ef0b]
Client Example: Grouped Convolution with Activation and Fusion
Theory
This client example demonstrates grouped convolution fused with various activation and elementwise operations. Grouped convolution splits the input and weights into groups and applies convolution independently to each group, while fusion with activation and scaling improves efficiency.
Mathematical Formulation:
For each group g:
- Convolution:
Y^g = \text{Conv}(X^g, W^g) - Fused operations:
E^g = f(Y^g, D_0^g, D_1^g, ...)fcan be bilinear, scale, add, relu, etc.
Algorithmic Background:
- Grouped convolution is used in efficient CNNs, depthwise separable convolutions, and expert models.
- Fused epilogue operations (scale, add, relu, reduce) are performed in registers before writing to memory.
- Supports 1D, 2D, and 3D grouped convolutions and a variety of fusion patterns.
How to Run
Prerequisites
Please follow the instructions in the main Build Guide section as a prerequisite to building and running this example.
Build and run
cd composable_kernel/client_example/24_grouped_conv_activation
mkdir build && cd build
cmake -DCMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc ..
make -j
# Example run (grouped conv + scale)
./grouped_convnd_fwd_scale/grouped_convnd_fwd_scale
# Example run (grouped conv + bilinear)
./grouped_convnd_fwd_bilinear/grouped_convnd_fwd_bilinear
# Example run (grouped conv + scale + relu)
./grouped_convnd_fwd_convscale_relu/grouped_convnd_fwd_convscale_relu
# Example run (grouped conv + scale + add + relu)
./grouped_convnd_fwd_scaleadd_scaleadd_relu/grouped_convnd_fwd_scaleadd_scaleadd_relu
Source Code Structure
Directory Layout
client_example/24_grouped_conv_activation/
├── grouped_convnd_fwd_scale/ # Grouped conv + scale
├── grouped_convnd_fwd_bilinear/ # Grouped conv + bilinear
├── grouped_convnd_fwd_convscale/ # Grouped conv + scale (convscale)
├── grouped_convnd_fwd_convscale_add/ # Grouped conv + scale + add
├── grouped_convnd_fwd_convscale_reduce/ # Grouped conv + scale + reduce
├── grouped_convnd_fwd_convscale_relu/ # Grouped conv + scale + relu
├── grouped_convnd_fwd_convinvscale/ # Grouped conv + inverse scale
├── grouped_convnd_fwd_scaleadd_ab/ # Grouped conv + scale + add (A/B)
├── grouped_convnd_fwd_scaleadd_scaleadd_relu/ # Grouped conv + scale + add + relu
├── grouped_convnd_bwd_data_bilinear/ # Grouped conv bwd data + bilinear
├── grouped_convnd_bwd_data_scale/ # Grouped conv bwd data + scale
├── grouped_convnd_bwd_weight_bilinear/ # Grouped conv bwd weight + bilinear
├── grouped_convnd_bwd_weight_scale/ # Grouped conv bwd weight + scale
├── CMakeLists.txt # Build configuration for the example
Key Functions
- main() (in each subdirectory's
.cpp):
Sets up input tensors, configures grouped convolution and fusion parameters, launches the kernel, and verifies the result. - Grouped convolution kernel invocation:
Uses the Composable Kernel device API to launch grouped convolution with various fused epilogue operations.
Additional Details
- Supports a wide range of fusion patterns (bilinear, scale, add, relu, reduce, etc.).
- Example parameters can be adjusted in the source for different workloads.
Related Examples
- 10_grouped_convnd_bwd_data: Grouped convolution backward data
- 11_grouped_conv_bwd_weight: Grouped convolution backward weight
- 30_grouped_conv_fwd_multiple_d: Grouped convolution forward with multiple D